基于神经网络模型的原子核基态自旋分布的随机相互作用研究
Random Interaction Study on Angular-momentum Distribution of Nucle-ar Ground State with Neural Networks
刘登 1ALAM Noor A 1肖越 1雷杨 2覃珍珍1
作者信息
- 1. 西南科技大学数理学院,四川绵阳 621010
- 2. 西南科技大学国防科技学院,四川绵阳 621010
- 折叠
摘要
利用神经网络模型学习、模拟随机两体系综(TBRE)下的原子核基态自旋分布,并对学习后的模型输入特征进行了分析.这是核物理中利用神经网络模型进行分类的典型应用.研究表明,采用本工作的单隐藏层神经网络模型,精确地描述每个随机相互作用系综内的样本仍比较困难.然而,神经网络模型却能够相对较好地描述基态自旋的统计性质,这可能是因为神经网络模型学习到了 TBRE中基态自旋分布的经验规律.
Abstract
The neural network model is used to learn and simulate the ground state spin distribution of the nucleus under stochastic two-system ensemble(TBRE),and the input characteristics of the learned model are analyzed.This is a typical ap-plication of classification using neural network models in nuclear physics.We show that it is still difficult to accurately each the sample within random interaction ensemble using the single hidden layer neural network model in this paper.However,the NN model describes the statistical properties of the ground state spins reasonably well,probably because the NN model learned the empirical law of the ground state spin distribution in TBRE.
关键词
神经网络/随机两体系综/原子核基态自旋Key words
neural network/two-body random ensemble/angular-momentum distribution of nuclear ground state引用本文复制引用
基金项目
国家自然科学基金资助项目(12105234)
出版年
2024
NETL
NSTL
万方数据